Landscape Metrics - “R As GIS” Course
--
https://jakubnowosad.com/rgis2025/output/landscape_metrics.html#/title-slide <-- shared presentation
--
#GIS #spatial #mapping #R #tutorial #onlinelearning #presentation #rasters #remotesensing #earthobservation #landscape #metrics #spatialanalysis #spatiotemporal #natural #social #cultural #ecosystems #biodiversity #water #waterquality #usecase #geomorphology #geomorphometry #patterns
Confused by R's loop syntax? 
Transform your understanding with clear examples and best practices for writing beautiful, maintainable repeat loops!
Read more: www.spsanderson.com/steveondata/...
#R #RStats #Programming #OpenData #RStats #Blog
Confused by R's loop syntax? Transform your understanding with clear examples and best practices for writing beautiful, maintainable repeat loops!
Read more: https://www.spsanderson.com/steveondata/posts/2025-04-07/
Confused by R's loop syntax? Transform your understanding with clear examples and best practices for writing beautiful, maintainable repeat loops!
Read more: https://www.spsanderson.com/steveondata/posts/2025-04-07/
Random Retro Game:
Title: R.B.I. Baseball
Released: 1988-01-01
Platforms: NES
Also released on: Genesis
#R.B.I.Baseball #NES #Retrogames
In order to open more the R project, I thought the next idea, which imply some homeworks for R package developers and R users doing research (to be published).
(A) For the researchers, they, always that possible, should 1) cite the main used R libraries for their research on their papers; and 2) make the R codes used for the (statistical) analyses computed for their research (papers) [and the tables and figures published] available (as Supplementary materials, as some VCS URL such as GitHub, GitLab, Codeberg... or by means of some repostory such as Zenodo, OSF..., or as persona websites...).
(B) And then, for package developers, it would be great that they have an updated vignette with examples of applications of their packages to research, at least for the most relevant applications (research done by relevant authors, in relevant journals, etc.)
Of course, (B) would use (A).
Random Retro Game:
Title: R.C. Grand Prix
Released: None
Platforms: SEGA Master System, Game Gear
#R.C.GrandPrix #SEGAMasterSystem #GameGear #Retrogames
Next at #WorkshopsForUkraine: Effective #DataVisualization #DataViz in #R #RStats in Scientific Contexts, by Christian Gebhard, Thursday April 10th, 6 pm CET. Register or sponsor a place for a student by donating to support #Ukraine. Details: https://sites.google.com/view/dariia-mykhailyshyna/main/r-workshops-for-ukraine#h.b0g432iynobt
Want to Work with Data? You NEED SQL!
Learn how to retrieve, filter, and manipulate databases efficiently
Speed up workflows & reduce dependency on others
Choose your path: SQL with Python or R
April 15, 2025 – 6 hours, online
#Rstats #BigData #SQL #R #Python
https://www.jumpingrivers.com/training/public/
@libreoffice We still see people recommending Excel and throwing around those "Excel tools" (aka spreadsheets with some formulas).
Please stop endorsing spreadsheet apps for anything else than spreadsheets. Better use applications for numerical computations like #NumeRe or #R, which produce understandable, mainainable and debuggable solutions.
This lesson mentions '#-' comments. Is that a special kind of #R comment that I don't know about?
https://swcarpentry.github.io/r-novice-inflammation/06-best-practices-R.html
Your project, python/R module/library, and install package should all use the same syntax/name/capitalization.
I will die on this hill. Just stop it.
Some #R #rstats about the packages on the repositories: #CRAN has 722 packages that use Bioconductor (3%), #Bioconductor has 2802 packages that use CRAN's packages (77%).
1.3% CRAN and 0.6% Bioconductor respectively use package outside these two repositories. But 91% and 93% use packages on their respective repos. 8% and 3% do not have any dependency on external packages (but could depend on system dependencies).
Random Retro Game:
Title: R.C. Grand Prix
Released: None
Platforms: SEGA Master System, Game Gear
#R.C.GrandPrix #SEGAMasterSystem #GameGear #Retrogames
Master while loops in R! Learn how to optimize performance, avoid common pitfalls, and write cleaner code. This guide is perfect for R programmers looking to enhance their skills.
Check it out: The Complete Guide to While Loops in R here: https://www.spsanderson.com/steveondata/posts/2025-03-31/
Master while loops in R! Learn how to optimize performance, avoid common pitfalls, and write cleaner code. This guide is perfect for R programmers looking to enhance their skills.
Check it out: The Complete Guide to While Loops in R here: https://www.spsanderson.com/steveondata/posts/2025-03-31/
Greg Cocks 
![library(rbenchmark)
library(dplyr)
# Initial Values
n <- 1:50000
value <- NULL
for(i in seq_along(n)) value[[i]] = n[[i]][[1]]
lapply(n, function (x) x[1])
lapply(n, \(x) x[1])
lapply(n, `[`, 1)
lapply(n, `[[`, 1)
# Benchmarking
benchmark(
`lapply` = lapply(n, function (x) x[1]),
`lapply with lambda` = lapply(n, \(x) x[1]),
`lapply with [ ]` = lapply(n, `[`, 1),
`lapply with [[ ]]` = lapply(n, `[[`, 1),
replications = 100,
columns = c("test", "replications", "elapsed", "relative")
) |>
arrange(relative)
test replications elapsed relative
1 lapply with [ ] 100 2.72 1.000
2 lapply with [[ ]] 100 2.75 1.011
3 lapply with lambda 100 4.90 1.801
4 lapply 100 5.10 1.875](https://cdn.masto.host/rstatsme/media_attachments/files/114/280/332/938/012/355/original/c344797945cc1932.png "library(rbenchmark)
library(dplyr)
# Initial Values
n <- 1:50000
value <- NULL
for(i in seq_along(n)) value[[i]] = n[[i]][[1]]
lapply(n, function (x) x[1])
lapply(n, \(x) x[1])
lapply(n, `[`, 1)
lapply(n, `[[`, 1)
# Benchmarking
benchmark(
`lapply` = lapply(n, function (x) x[1]),
`lapply with lambda` = lapply(n, \(x) x[1]),
`lapply with [ ]` = lapply(n, `[`, 1),
`lapply with [[ ]]` = lapply(n, `[[`, 1),
replications = 100,
columns = c(\"test\", \"replications\", \"elapsed\", \"relative\")
) |>
arrange(relative)
test replications elapsed relative
1 lapply with [ ] 100 2.72 1.000
2 lapply with [[ ]] 100 2.75 1.011
3 lapply with lambda 100 4.90 1.801
4 lapply 100 5.10 1.875")
![library(rbenchmark)
library(dplyr)
# Initial Values
n <- 1:50000
value <- NULL
for(i in seq_along(n)) value[[i]] = n[[i]][[1]]
lapply(n, function (x) x[1])
lapply(n, \(x) x[1])
lapply(n, `[`, 1)
lapply(n, `[[`, 1)
# Benchmarking
benchmark(
`lapply` = lapply(n, function (x) x[1]),
`lapply with lambda` = lapply(n, \(x) x[1]),
`lapply with [ ]` = lapply(n, `[`, 1),
`lapply with [[ ]]` = lapply(n, `[[`, 1),
replications = 100,
columns = c("test", "replications", "elapsed", "relative")
) |>
arrange(relative)
test replications elapsed relative
1 lapply with [ ] 100 2.72 1.000
2 lapply with [[ ]] 100 2.75 1.011
3 lapply with lambda 100 4.90 1.801
4 lapply 100 5.10 1.875](https://media.mstdn.social/media_attachments/files/114/280/332/699/357/109/original/660198e41a5d5f03.png "library(rbenchmark)
library(dplyr)
# Initial Values
n <- 1:50000
value <- NULL
for(i in seq_along(n)) value[[i]] = n[[i]][[1]]
lapply(n, function (x) x[1])
lapply(n, \(x) x[1])
lapply(n, `[`, 1)
lapply(n, `[[`, 1)
# Benchmarking
benchmark(
`lapply` = lapply(n, function (x) x[1]),
`lapply with lambda` = lapply(n, \(x) x[1]),
`lapply with [ ]` = lapply(n, `[`, 1),
`lapply with [[ ]]` = lapply(n, `[[`, 1),
replications = 100,
columns = c(\"test\", \"replications\", \"elapsed\", \"relative\")
) |>
arrange(relative)
test replications elapsed relative
1 lapply with [ ] 100 2.72 1.000
2 lapply with [[ ]] 100 2.75 1.011
3 lapply with lambda 100 4.90 1.801
4 lapply 100 5.10 1.875")
